In some regions of the world, commercial heavy vehicle drivers are required to rest and take breaks to mitigate driving accidents while fatigued. Such rules must be balanced against on-time delivery requirements as well as shorter lead times to plan their trips, thereby making location and parking availability of rest area facilities more critical.
In the United States, federal hours of service (HOS) rules have reduced the risk of Commercial Heavy Vehicle (CHV) crashes where driver fatigue has been shown to be a contributing factor.
Although there are many rest area facilities available to truck drivers, a common problem is the difficulty of obtaining information on truck parking availability for these facilities. Privately owned truck stop facilities may have available spaces, but in the absence of accurate and timely parking information, drivers have perceived a lack of available parking and a driver may opt to drive fatigued or park illegally (on highway shoulders or ramps)—both of which are safety hazards.
Increasing the number of truck stops and rest areas, or adding capacity to existing ones, may be either financially intractable, or incompatible with local restrictions. The existing truck parking capacity is not efficiently utilized; some remain almost empty while others are near, or over, capacity. High occupancy levels at rest areas have also been correlated with an increase in truck crash frequency on adjacent highway segments.
Real-time parking monitoring technologies can be described as indirect and direct. Indirect technologies are based on detecting and classifying vehicles at all ingress and egress points of the parking facility and summing the difference over accumulated counts at specified time intervals. The general problem with technologies based on ingress-egress count detection is that small counting and vehicle classification errors can accumulated over time. One example of an indirect technology includes battery-powered magnetometers embedded in the pavement at the egress and ingress locations of the parking facility to estimate occupancy by subtracting the two counts in real-time. Another example of an indirect technology uses camera sensors at the entrance and exit to a parking facility. The camera sensors utilize “trip-wire” detectors to sense vehicle presence and motion, and vehicle length classification. The detection accuracy can be impaired by such factors as poor lighting, vehicle color, shadows, headlights, and flying birds. Furthermore, “trip-wire” counting cannot determine actual occupancy for undisciplined parking, which occurs when drivers straddle parking lane line designations, differ in their maneuvering skills, or where lanes are not delineated.
Various methodologies directly monitor parking spaces using camera sensors. Some have used a foreground/background blob segmentation algorithm based on time-variant mixture of Gaussians combined with shadow removal. One approach ortho-rectifies a 2D camera view of vehicle parking spaces into a top-down viewpoint before segmenting each parking space. A sliding window is passed over the lot to encode the detection result based on probabilities of occupancy using mean color of the space compared to an a priori color feature of the empty space. Other approaches entail computing color histograms of parking space regions defined a priori or using aerial images to train an SVM linear classifier. The performance under various lighting conditions and in view of object occlusion challenges may be problematic.
There are a number of challenges with such 2D image computer vision techniques arising from rapid changes in background illumination, glare, and partial occlusions from overlapping vehicles. These challenges are aggravated with commercial heavy vehicles because of their larger size and height compared to private vehicles.